The Impact of Implementing a Comprehensive Surgical Program on the Surgical Cohort at a Remote Referral Hospital in Southeastern Liberia
Bibliographic record
Abstract
BACKGROUND: Liberia has an extreme health workforce shortage, particularly with respect to surgery. JJ Dossen Memorial (JJD) is a public referral hospital supported by Partners in Health. METHODS: We designed and implemented a comprehensive surgical program at JJD. Using case logs, clinic records, and transfer data between December 2016 and April 2018, we evaluated the impact of this program on the surgical cohort and examined temporal trends in patient origin using GIS. RESULTS: The mean number of cases per day increased from 1.7 ± 1.0 to 2.4 ± 1.3 (p < 0.001). The proportion of females decreased from 59.8 to 51.2% (p = 0.03), and mean age decreased from 32.2 ± 14.2 to 29.8 ± 16.5 years (p = 0.05). The proportion of elective procedures, C-sections, and laparotomies did not change, but hernias decreased from 28.9 to 22.3% (p = 0.05) and oncologic surgery increased from 0.0 to 5.6% (p < 0.001). A smaller proportion of cases were performed under local or general anesthesia, while a larger proportion were performed under spinal and sedation (p < 0.001). Outward surgical transfers decreased from 13.1 to 5.4% (p < 0.001). The mean distance from patient residence to JJD increased from 24.8 ± 29.0 to 32.3 ± 41.9 km (p = 0.01). GIS analysis revealed a broader distribution of patient origins. CONCLUSIONS: Surgeons are desperately needed in referral hospitals to address the large burden of surgical disease in Liberia. The implementation of a surgical program significantly changed the demographics of the surgical cohort and the surgical case mix. Our data can inform training for health workers in Liberia and elsewhere.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".